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arXiv · 2607.22625

TokenMem: Faithful Knowledge Injection for Frozen LLMs

Chengzhang Yu·Chenyang Zheng·Zening Lu·Yingru He·Yutong Huang·Yiming Zhang·Yue Xu·Zhanpeng Jin
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Abstract

Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge, but suffers from knowledge conflicts: when retrieved information contradicts parametric memory, the shared self-attention pathway produces unpredictable outputs. We present TokenMem, a lightweight memory system that injects knowledge into frozen LLMs through a dedicated cross-attention channel, bypassing competition with parametric memory in the residual stream. TokenMem trains only a thin gating adapter ($\sim$3-7M parameters) via a two-phase curriculum: first learning general knowledge utilization, then strengthening faithful compliance under counterfactual knowledge. In controlled experiments on five models spanning three families (Qwen3-4B/8B/14B, LLaMA-3.1-8B, OLMo-3-7B), TokenMem achieves 69-70% Knowledge Compliance (KC) on counterfactual benchmarks, compared to 20-52% for vanilla RAG, a gap of up to 49 percentage points. Ablation studies show that the two-phase curriculum is critical: removing Phase 2 collapses KC to near-zero. Mechanistic analysis reveals that the gate adapter learns a conflict-aware, layer-specific injection strategy without explicit supervision.

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Chengzhang Yu, Chenyang Zheng, Zening Lu, Yingru He, Yutong Huang, Yiming Zhang, Yue Xu, Zhanpeng Jin. 2026-06-17. TokenMem: Faithful Knowledge Injection for Frozen LLMs. https://arxiv.org/abs/2607.22625

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